Computational thinking (CT), which is a cognitive skill used to solve problems with computational solutions, has drawn increasing attention among researchers and practitioners due to the growing recognition of CT competence as a 21st century skill. Collaboration is commonly integrated into CT education to facilitate novice learning, but there is inadequate knowledge regarding the influences of collaboration in CT education. This meta-analysis examined the overall effects on the cognitive, social and affective competencies of collaborative versus individual problem solving in CT through programming. We identified 33 publications involving 4717 learners, which allowed for 220 effect size comparisons. We found a medium effect size (Hedges' g = 0.562; p < 0.001) in favour of collaborative problem solving on cognitive learning outcomes and a small effect size (Hedges' g = 0.316; p < 0.01) on affective learning outcomes using a randomeffects model. Categorical moderator analysis revealed the moderating effects of educational level, programming environment, study duration, grouping method and group size. The competency model that was generated from the synthesized literature on

Legal Judgement Prediction has attracted more and more attention in recent years. One of the challenges is how to design a model with better interpretable prediction results. Previous studies have proposed different interpretable models based on the generation of court views and the extraction of charge keywords. Different from previous work, we propose a multi-task legal judgement prediction model which combines a subtask of the seriousness of charges. By introducing this subtask, our model can capture the attention weights of different terms of penalty corresponding to the charges and give more attention to the correct terms of penalty in the fact descriptions. Meanwhile, our model also incorporates the position of defendant making it capable of giving attention to the contextual information of the defendant. We carry several experiments on the public CAIL2018 dataset. Experimental results show that our model achieves better or comparable performance on three subtasks compared with the baseline models. Moreover, we also analyze the interpretable contribution of our model.

BackgroundCollaboration has commonly been integrated in computational thinking education to foster novice learning. While the majority of existing studies on developing students' CT skills have been based on competency or personality factors, social factors have been largely ignored.ObjectivesThis systematic review, framed by social cognitive learning theories, aims to unravel the influencing factors of collaboration in developing computational thinking skills in K‐12 education.MethodsWe searched four databases and located 79 publications for synthesis analysis to identify 10 social cognitive factors from personal and learning environmental perspectives. Personal factors are gender, prior knowledge, and motivational attitudes, while the learning environmental factors are roles, partnership, interaction, culture, tools, tasks, and scaffolding.Results and ConclusionsWe examined the following five major issues: (a) collaboration elements designed in learning activities; (b) social cognitive factors that might influence the development of computational thinking; (c) the influences caused by social cognitive factors; (d) students' perceived benefits of collaboration; (e) major challenges of integrating collaboration in computational thinking education. The most reported challenges in collaborative classrooms are students' misunderstanding of computational thinking knowledge, negative sentiments, and communication problems. Accordingly, a design framework to facilitate collaborative learning activities was proposed. The design framework can provide a more focused research avenue for examining the effects of collaboration in the development of computational thinking in K‐12 education.

Background: Computational thinking (CT) is regarded as an essential 21st-century skill, and attempts have been made to integrate it into other subjects. Instructional approaches to CT development and assessment in the field of computer science have attracted global attention, but the influence of CT skills on other subject areas is under-researched.Objective: Our goal is to investigate the transfer effects of CT in different subject areas and examine the educational characteristics of CT intervention approaches that promote the transfer of learning.Method: We carefully selected and reviewed 55 empirical studies from leading bibliographic databases and examined the transfer of CT using a meta-analysis and a qualitative synthesis. Results and Conclusions:We identified and summarized these effects in the fields of mathematics, science, engineering and the humanities. A meta-analysis of these studies identified a generally significant effect of the transfer of CT skills to other subject areas. We also explored the characteristics of CT interventions that aid the transfer of learning by qualitatively assessing the identified studies. The results of the review offer a holistic view of the trends in CT transfer research that can be used as a reference for both researchers and instructors. K E Y W O R D S computational thinking, meta-analysis, qualitative synthesis, transfer of learning 1 | INTRODUCTION Computational thinking (CT) has become a key motivator for bringing computer science back into K-12 schools (Tikva & Tambouris, 2021).CT is initially defined as a set of cognitive problem-solving skills originating from computer science (Wing, 2006), and gradually the notion of CT has been developed into a new cross-disciplinary literacy, which can be a vehicle for personal expression and can connect with other literacy practices (Kafai & Proctor, 2021). Grover and Pea (2013)

Students’ perceptions of learning are important predictors of their learning motivation and academic performance. Examining perceptions of learning has meaningful implications for instruction practices, while it has been largely neglected in the research of computational thinking (CT). To contribute to the development of CT education, we explored the influence of students’ perceptions on their motivation and performance in CT acquisition and examined the gender difference in the structural model using a multigroup structural equation modeling (SEM) analysis. Two hundred and eighty-five students from a Chinese urban high school were recruited for the study. The analysis revealed that students’ perceptions of CT positively influenced their CT performance and learning motivation, and some motivational constructs, namely self-efficacy and learning goal orientation (LGO), also positively influenced their CT performance. Furthermore, in the male student group, perceptions of CT exhibited significant correlations with both self-efficacy and LGO. However, no significant correlation was found in the female student group. Implications for research and teaching practice in CT education are presented herein.